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An evaluation of efficiency in dairy production using structural equation modelling

Published online by Cambridge University Press:  17 December 2018

J. Drews*
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, D-24098 Kiel, Germany
I. Czycholl
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, D-24098 Kiel, Germany
W. Junge
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, D-24098 Kiel, Germany
J. Krieter
Affiliation:
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Olshausenstr. 40, D-24098 Kiel, Germany
*
Author for correspondence: J. Drews, E-mail: [email protected]

Abstract

Optimization of production factors plays a central role in efficient milk production operations. Causal relationships between production parameters (health, fertility, feeding, performance and farm size) on the one hand and efficiency parameters on the other have been identified in several studies. In recent years, structural equation modelling (SEM) has not only gained importance in agriculture but also in milk production, providing the opportunity to investigate multilateral relationships. Additionally, SEM enables an estimation of parameters which are not themselves measurable, the so-called latent variables. The current study was based on the data of 943 branch settlements (including the years 2012 and 2013) of dairy farms keeping German Holstein cows in Schleswig-Holstein (Northern Germany) which provided a combination of the structural parameters, economic parameters and biological performance of the farms. An SEM using this combined data was applied to investigate the complexity of influences on efficiency parameters in milk production. Efficiency was sub-divided into and evaluated by two effect variables (economic efficiency and biological efficiency). Economic efficiency was defined as a conventional efficiency assessment criterion from full-cost accounting, whereas biological efficiency was used to evaluate the quality of herd management. Performance was identified as the key parameter for independent evaluation of efficiency by assessing biological (γ41 = 0.644) or economic efficiency (γ42 = 0.266). The SEM explained more than three times higher proportion of the variance in biological efficiency than in economic efficiency. The investigation proved the eligibility of partial least squares SEM for the evaluation of efficiency in milk production.

Type
Modelling Animal Systems Research Paper
Copyright
Copyright © Cambridge University Press 2018 

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References

Bailey, K, Hardin, D, Spain, J, Garrett, J, Hoehne, J, Randle, R, Ricketts, R, Steevens, B and Zulovich, J (1997) An economic simulation study of large-scale dairy units in the Midwest. Journal of Dairy Science 80, 205214.Google Scholar
Chin, W (1998) The partial least squares approach to structural equation modeling. In Marcoulides, GA (ed.) Modern Methods for Business Research. Mahwah, NJ, USA: Lawrence Erlbaum Associates, pp. 295336.Google Scholar
Davison, AC and Hinkley, DV (1997) Bootstrap Methods and Their Application. Cambridge, UK: Cambridge University Press.Google Scholar
de los Campos, G, Gianola, D and Heringstad, B (2006) A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows. Journal of Dairy Science 89, 44454455.Google Scholar
De Vries, A (2006) Economic value of pregnancy in dairy cattle. Journal of Dairy Science 89, 38763885.Google Scholar
Efron, B and Tibshirani, RJ (1993) An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability 57. Dordrecht, The Netherlands: Springer Science + Business Media.Google Scholar
Fornell, C and Larcker, DF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 3950.Google Scholar
Groenendaal, H, Galligan, DT and Mulder, HA (2004) An economic spreadsheet model to determine optimal breeding and replacement decisions for dairy cattle. Journal of Dairy Science 87, 21462157.Google Scholar
Hair, JF, Hult, G, Ringle, C and Sarstedt, M (2014) A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: SAGE Publications, Inc.Google Scholar
Hansen, LB (2000) Consequences of selection for milk yield from a geneticist's viewpoint. Journal of Dairy Science 83, 11451150.Google Scholar
Henseler, J, Ringle, CM and Sinkovics, RR (2009) The use of partial least squares path modeling in international marketing. In Sinkovics, RR and Ghauri, PN (eds), New Challenges to International Marketing (Advances in International Marketing, Volume 20). Bingley, UK: Emerald Group Publishing Limited, pp. 277319.Google Scholar
Heringstad, B, Wu, XL and Gianola, D (2009) Inferring relationships between health and fertility in Norwegian Red cows using recursive models. Journal of Dairy Science 92, 17781784.Google Scholar
Huhtanen, P and Nousiainen, J (2012) Production responses of lactating dairy cows fed silage-based diets to changes in nutrient supply. Livestock Science 148, 146158.Google Scholar
Klarmann, M (2008) Methodische Problemfelder der Erfolgsfaktorenforschung. Bestandsaufnahme und empirische Analysen. Wiesbaden, Germany: Springer Gabler.Google Scholar
Knapp, JR, Laur, GL, Vadas, PA, Weiss, WP and Tricarico, JM (2014) Invited review: enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science 97, 32313261.Google Scholar
Kvapilik, J, Hanuš, O, Bartoň, L, Klimešová, MV and Roubal, P (2015) Mastitis of dairy cows and financial losses: an economic meta-analysis and model calculation. Bulgarian Journal of Agricultural Science 21, 10921105.Google Scholar
Lagomasino, IT, Zatzick, DF and Chambers, DA (2010) Efficiency in mental health practice and research. General Hospital Psychiatry 32, 477483.Google Scholar
Lamb, E, Shirtliffe, S and May, W (2011) Structural equation modeling in the plant sciences. An example using yield components in oat. Canadian Journal of Plant Science 91, 603619.Google Scholar
MacDonald, JM, O'Donoghue, EJ, McBride, WD, Nehring, RF, Sandretto, CL and Mosheim, R (2007) Profits, Costs, and the Changing Structure of Dairy Farming. Economic Research Report no. 47. Washington, DC: USDA.Google Scholar
Marston, SP, Clark, GW, Anderson, GW, Kersbergen, RJ, Lunak, M, Marcinkowski, DP, Murphy, MR, Schwab, CG and Erickson, PS (2011) Maximizing profit on New England organic dairy farms. An economic comparison of 4 total mixed rations for organic Holsteins and Jerseys. Journal of Dairy Science 94, 31843201.Google Scholar
Martin, P and Bateson, P (2007) Measuring Behaviour: An Introductory Guide. Cambridge, UK: Cambridge University Press.Google Scholar
Mathijs, E and Swinnen, JFM (2001) Production organization and efficiency during transition: an empirical analysis of East German agriculture. Review of Economics and Statistics 83, 100107.Google Scholar
Mgbeahuruike, AC, Nørgaard, P, Eriksson, T, Nordqvist, M and Nadeau, E (2016) Faecal characteristics and milk production of dairy cows in early-lactation fed diets differing in forage types in commercial herds. Acta Agriculturae Scandinavica, Section A – Animal Science 66, 816.Google Scholar
Mitev, J, Gergovska, ZH, Miteva, TCH and Penev, T (2011) Influence of lameness on daily milk yield, lactation curve and body condition score during lactation in black-and white cows. Bulgarian Journal of Agricultural Science 17, 704711.Google Scholar
Moran, J (2005) Economics of feeding dairy cows. In Moran, J (ed.) Tropical Dairy Farming: Feeding Management for Small Holder Dairy Farmers in the Humid Tropics. Collingwood, Australia: Landlinks Press, pp. 191208.Google Scholar
Nitzl, C (2010) Eine Anwenderorientierte Einführung in die Partial Least Square (PLS)-Methode. Arbeitspapier Nr. 21. Hamburg, Germany: Inst. für Industriebetriebslehre und Organisation.Google Scholar
Ombao, H, Lindquist, M, Thompson, W and Aston, J (2017) Handbook of Neuroimaging Data Analysis. Boca Raton, FL: CRC Press.Google Scholar
Ringle, CM, Wende, S and Becker, J-M (2015) SmartPLS 3. Bönningstedt, Germany: SmartPLS. Available at http://www.smartpls.com (Accessed 31/10/18).Google Scholar
SAS Institute Inc. (2008) SAS/STAT (R) User's Guide, Version 9.2. Cary, NC: SAS Institute Inc.Google Scholar
Shook, GE (2006) Major advances in determining appropriate selection goals. Journal of Dairy Science 89, 13491361.Google Scholar
Stankov, E, Stoyanova, S and Petrov, D (2015) Regression analysis of profit per 1 kg milk produced in selected dairy cattle farms. International Journal of Current Microbiology and Applied Science 4, 713719.Google Scholar
Steinfeld, H, Wassenaar, T and Jutzi, S (2006) Livestock production systems in developing countries: status, drivers, trends. Scientific and Technical Review of the Office International des Epizooties (Paris) 25, 505516.Google Scholar
Valente, BD, Rosa, GJM, Gianola, D, Wu, X-L and Weigel, K (2013) Is structural equation modeling advantageous for the genetic improvement of multiple traits? Genetics 194, 561572.Google Scholar
VanRaden, PM (2004) Invited review: selection on net merit to improve lifetime profit. Journal of Dairy Science 87, 31253131.Google Scholar
Wangler, A and Harms, J (2006) Verlängerung der Nutzungsdauer der Milchkühe durch eine gute Tiergesundheit bei gleichzeitig hoher Lebensleistung zur Erhöhung der Effizienz des Tiereinsatzes. Forschungsbericht. Dummerstorf, Germany: Forschungsbericht der Landesforschungsanstalt für Landwirtschaft und Fischerei Mecklenburg-Vorpommern, Institut für Tierproduktion.Google Scholar
Weersink, A and Tauer, LW (1991) Causality between dairy farm size and productivity. American Journal of Agricultural Economics 73, 11381145.Google Scholar
Wold, S (1994) PLS for multivariate linear modeling. In van der Waterbeemd, H (ed). QSAR: Chemometric Methods in Molecular Design: Methods and Principles in Medicinal Chemistry. Weinheim, Germany: Verlag-Chemie, pp. 195218.Google Scholar
Wright, S (1921) Correlation and causation. Journal of Agricultural Research 20, 557585.Google Scholar